proof blockPage 468%
This equation captures one of the core mathematical components of the system. sequence length and d is the hidden dimension. The shadow state s(ℓ) ∈RT ×d serves as a depth-
ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning explores ShadowPEFT offers a centralized parameter-efficient fine-tuning framework for LLMs that improves adaptation flexibility and performance.. Commercial viability score: 7/10 in LLM Fine-tuning.
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This route is the stable paper-level surface for citations, viability, references, and downstream handoffs. Use it as the proof layer behind Signal Canvas, workspace creation, and launch-pack generation.
Page Freshness
Canonical route: /paper/shadowpeft-shadow-network-for-parameter-efficient-fine-tuning
This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.
Agent Handoff
Canonical ID shadowpeft-shadow-network-for-parameter-efficient-fine-tuning | Route /paper/shadowpeft-shadow-network-for-parameter-efficient-fine-tuning
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/shadowpeft-shadow-network-for-parameter-efficient-fine-tuningMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.19254"
}
}source_context
{
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"query": "ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning",
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"paper_ref": "shadowpeft-shadow-network-for-parameter-efficient-fine-tuning",
"topic_slug": null,
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}Paper proof page receipt window
/buildability/shadowpeft-shadow-network-for-parameter-efficient-fine-tuning
Subject: ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning
Verdict
Watch
Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.
Time to first demo
Insufficient data
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Structured compute envelope
Insufficient data
No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.
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Dimensions overall score 7.0
Visual citation anchors from the paper document graph.
This equation captures one of the core mathematical components of the system. sequence length and d is the hidden dimension. The shadow state s(ℓ) ∈RT ×d serves as a depth-
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Receipt path
/buildability/shadowpeft-shadow-network-for-parameter-efficient-fine-tuning
Paper ref
shadowpeft-shadow-network-for-parameter-efficient-fine-tuning
arXiv id
2604.19254
Generated at
2026-04-22T02:13:59.908Z
Evidence freshness
stale
Last verification
2026-04-22T02:13:59.908Z
Sources
4
References
6
Coverage
67%
Lineage hash
77ec7b49c3732f55d60a246f518c656b75e4bb167ce891731290a63f90520198
Canonical opportunity-kernel lineage hash.
External signature
unsigned_external
No founder, registry, pilot, or production-adoption signature is attached to this receipt.
Verification
not_verified
Verification is blocked until an external signature is provided.
6 refs / 4 sources / Verification pending
repo_url
proof_status
Page and bbox are available; crop image is pending.
This equation describes how the model state or parameters are updated from one step to the next.
Page and bbox are available; crop image is pending.
This equation captures one of the core mathematical components of the system. out ∈RT ×d denote the hidden state of the ℓ-th LLM decoder layer (base layer), where T is the
Page and bbox are available; crop image is pending.
No public competitor map is available for this paper yet.